Key Takeaways

  • 75% of U.S. employers use automated applicant tracking systems to screen resumes before a human reviews them (Harvard Business School & Accenture, 2021)
  • The most common ATS failures are missing keywords, incompatible formatting, and incorrect file types
  • ResumeGeni scores your resume across 8 parsing layers — modeled on the same steps enterprise ATS platforms like Workday, Greenhouse, and Taleo use to evaluate candidates

How ATS Resume Scoring Works

Applicant tracking systems parse your resume into structured data — extracting your name, contact info, work history, skills, and education — then score how well that data matches the job requirements. Many ATS rejections happen because the parser couldn't extract critical fields, not because the candidate wasn't qualified.

LayerWhat It ChecksWhy It Matters
Document extractionFile format, encoding, readabilityCorrupted or image-only PDFs fail immediately
Layout analysisTables, columns, headers, footersMulti-column layouts break field extraction
Section detectionExperience, education, skills headingsNon-standard headings cause sections to be missed
Field mappingName, email, phone, dates, titlesMissing contact info is a common cause of immediate rejection
Keyword matchingJob-specific terms, skills, certificationsKeyword overlap affects recruiter search visibility and ATS scoring
Chronology checkDate ordering, gap detectionReverse-chronological order is expected by most ATS
QuantificationMetrics, numbers, measurable outcomesQuantified achievements help human reviewers and some scoring models
Confidence scoringOverall parse quality and completenessLow-confidence parses get deprioritized in results

Frequently Asked Questions

Is ResumeGeni free?
Yes. ResumeGeni is currently in beta — ATS analysis, scoring, and initial improvement suggestions are free with no signup required. Full guidance and saved reports may require a free account.
What file formats are supported?
PDF, DOCX, DOC, TXT, RTF, ODT, and Apple Pages. PDF and DOCX are recommended for best ATS compatibility.
How is the ATS score calculated?
Your resume is processed through an 8-layer parsing pipeline that extracts structured data the same way enterprise ATS platforms do. The score reflects how completely and accurately your resume can be parsed, plus how well your content matches common ATS ranking criteria.
Can ATS read PDF resumes?
Yes, but not all PDFs are equal. Text-based PDFs parse well. Image-only PDFs (scanned documents) and PDFs with complex tables or multi-column layouts often fail ATS parsing. Our analyzer will flag these issues.
How do I improve my ATS score?
Focus on three areas: use a clean single-column format, include keywords from the job description naturally in your experience bullets, and ensure all sections (contact, experience, education, skills) use standard headings.

ATS Guides & Resources

Built by engineers with 12 years of experience building enterprise hiring technology at ZipRecruiter. Last updated .

Data Engineer

Digital Forms · Poland

Join our team as a Data Engineer! Design and implement robust data pipelines, build scalable data lakes, and enable business insights through efficient data systems. Work remotely from Poland on a B2B contract.

Requirements

Responsibilities

  • Build and maintain scalable data pipelines (ETL/ELT) to collect, transform, and integrate data from various systems into a centralized Data Lake.

  • Design and optimize data architectures for reporting and analytics purposes.

  • Ensure data quality, security, and availability across systems.

  • Collaborate with analysts and stakeholders to define data requirements and support business intelligence efforts.

  • Monitor and troubleshoot data pipelines to ensure smooth operation.

  • Stay updated on industry trends and best practices in data engineering.

Must-Have Requirements

Hard Skills:

  • Proven experience in designing and maintaining data pipelines (e.g., Apache Airflow, Talend, or similar tools).

  • Strong knowledge of SQL and programming languages such as Python.

  • Experience with data lakes and data warehouses (e.g., Snowflake, AWS S3, or BigQuery).

  • Familiarity with cloud platforms (AWS, GCP, Azure) and related services for data processing.

  • Understanding of data modeling and optimizing database performance.

Soft Skills:

  • Analytical mindset with a problem-solving approach.

  • Strong communication skills to work effectively with technical and non-technical stakeholders.

  • Process-oriented and detail-focused, with an emphasis on quality.

Availability

  • Must be available for a 4-hour overlap between 15:00 and 19:00 Poland time to ensure real-time collaboration.

Highlights

Build robust data pipelines, shape data infrastructure, and work remotely with a global team. Flexible B2B contract and opportunities to grow in the field of data engineering.

Originally posted on Himalayas